encodingai
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electra-base-discriminator-im-multilabel-V2

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 ---
 library_name: transformers
-tags: []
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 ---
+license: cc-by-nc-nd-4.0
+language:
+- en
 library_name: transformers
+pipeline_tag: text-classification
+widget:
+- text: "Mr. Jones, an architect is going to surprise his family by building them a new house."
+  example_title: "Pow"
+- text: "They want the research to go well and be productive."
+  example_title: "Ach"
+- text: "The man is trying to see a friend on board, but the officer will not let him go as the whistle for all ashore who are not going has already blown."
+  example_title: "Aff"
+- text: "The recollection of skating on the Charles, and the time she had pushed me through the ice, brought a laugh to the conversation; but it quickly faded in the murky waters of the river that could no longer freeze over."
+  example_title: "Pow + Aff"
+- text: "They are also well-known research scientists and are quite talented in this field."
+  example_title: "Pow + Ach"
+- text: "After a nice evening with his family, he will be back at work tomorrow, doing the best job he can on his drafting."
+  example_title: "Ach + Aff"
+- text: "She is surprised that she is able to make these calls and pleasantly surprised that her friends respond to her request."
+  example_title: "Pow + Aff"
 ---
+This is an updated version of [https://huggingface.co/encodingai/electra-base-discriminator-im-multilabel](https://huggingface.co/encodingai/electra-base-discriminator-im-multilabel),
+reported in [Pang & Ring (2020)](https://rdcu.be/b38pm)
+and found at [implicitmotives.com](https://implicitmotives.com). The classifier identifies the
+presence of implicit motive imagery in sentences, namely the three felt needs for Power, Achievement,
+and Affiliation.
+The current classifier is finetuned from ELECTRA-base and achieves > 0.91 ICC on the
+Winter (1994) training data (see the [OSF repo](https://osf.io/aurwb/) for the benchmark dataset).
+Development of this classifier is ongoing, and the current version has been trained on a larger and
+more diverse dataset, which means it generalizes better to unseen data.
+This model is being made available to other researchers for inference via a Huggingface api. The
+current license allows for free use without modification for non-commercial purposes. If you would
+like to use this model commercially, get in touch with us for access to our most recent model.
+```
+Predictions on Winter manual dataset
+-----
+Intra-class Correlation Coefficient:
+| Pow (Label_0): | 0.91799 |
+| Ach (Label_1): | 0.92512 |
+| Aff (Label_2): | 0.89165 |
+| mean: | 0.91147 |
+Pearson correlations:
+| Pow (Label_0): 0.8485 |
+| Ach (Label_1): 0.86187 |
+| Aff (Label_2): 0.80574 |
+| mean: 0.83836 |
+```
+## Inference guide
+The inference api requires a Huggingface token. The sample code below illustrates how it can be used to classify individual sentences.
+```python
+import json
+import requests
+api_key = "<HF Token>"
+headers = {"Authorization": f"Bearer {api_key}"}
+api_url = "https://api.url.here"
+# This is a sentence from the Winter manual that is dual-scored for both Pow and Aff
+prompt = """The recollection of skating on the Charles, and the time she had
+            pushed me through the ice, brought a laugh to the conversation; but
+            it quickly faded in the murky waters of the river that could no
+            longer freeze over."""
+# Since this is a multilabel classifier, we want to return scores for the top 3 labels
+data = {"inputs": prompt, "parameters": {"top_k": 3}}
+response = requests.request("POST", api_url, headers=headers, json=data)
+# The labels are arranged according to likelihood of classification
+repdict = {"LABEL_0": "Pow", "LABEL_1": "Ach", "LABEL_2": "Aff"}
+# so we replace them in the output
+scores = {repdict[x['label']]: x['score'] for x in response.json()}
+print(scores)
+# output: {'Pow': 0.8279141187667847, 'Aff': 0.7250356674194336, 'Ach': 0.0020263446494936943}
+```
+## References
+McClelland, D. C. (1965). Toward a theory of motive acquisition. American Psychologist, 20,321-333.
+Pang, J. S., & Ring, H. (2020). Automated Coding of Implicit Motives: A Machine-Learning Approach.
+Motivation and Emotion, 44(4), 549-566. DOI: 10.1007/s11031-020-09832-8.
+Winter, D.G. (1994). Manual for scoring motive imagery in running text. Unpublished Instrument. Ann Arbor: University of Michigan.